This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applied to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities. The methodology demonstrates a significant enhancement in anomaly detection, evidenced by a notable correlation of 0.45 between the reconstruction error and the proposed metric. Our findings highlight the potential of employing VAEs for more accurate and trustworthy anomaly detection in network security.
@article{arxiv.2409.13774,
title = {Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space},
author = {Ioannis Pitsiorlas and George Arvanitakis and Marios Kountouris},
journal= {arXiv preprint arXiv:2409.13774},
year = {2024}
}